Anchor-based methods, also known as top-down methods, generate temporal proposals by assigning dense and multi-scale intervals with pre-defined lengths to uniformly distributed temporal locations in the input video. Formally, given a video with \(T\)  frames, \(\frac{T}{\sigma }\)  temporal positions, known as anchors, are uniformly sampled from every \(\sigma \)  frames. Then, several temporal windows with different duration \(\lbrace d_1,d_2, \cdots , d_n\rbrace \)  are centered around each anchor as initial temporal proposals. The proposal lengths (\(d_i\)  s) must have a wide range to align with action instances of various lengths that can last from less than a second to several minutes in untrimmed videos [1]}. Then visual encoders and convolution layers are applied on the temporal proposals for feature extraction and features are used to evaluate the quality of temporal proposals and adjust their boundaries (Section REF ).
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